Thermal Emission Imaging System (THEMIS) and Thermal Emission Spectrometer (TES) are orbital multispectral imagers that can be used for surface characterization of Mars. THEMIS has 10 spectral bands in the 6-13 micrometers region and a spatial resolution of 100 m. TES has 143 spectral bands in the 5-50 micrometers range, but with low spatial resolution of 3×6 km. Although all of them have been used to map out the surface characteristics of Mars, there are some limitations. First, THEMIS has low spectral resolution that may not provide accurate surface characterization. Second, TES has low spatial resolution that cannot provide fine details of surface characteristics. FIG. 1 illustrates the spatial difference between THEMIS and TES images. Roughly speaking, each TES pixel contains about 900 THEMIS pixels.
For Earth observations, there are imagers that are like the above instruments for Mars. For example, the Worldview-3 imager collects high resolution visible and short-wave infrared (SWIR) images at sub-meter resolution whereas the NASA's Moderate Resolution Imaging Spectroradiometer (MODIS), NOAA's Advanced Very High Resolution Radiometer (AVHRR), etc. are collecting low resolution (hundreds of meters) multispectral images. For some future hyperspectral imagers like the NASA Hyperspectral Infrared Imager (HyspIRI) with hundreds of bands, the spatial resolution is only about 30 meters. It is advantageous to fuse the high-resolution Worldview images with MODIS, AVHRR, and HyspIRI images to yield high resolution in both spatial and spectral domains. Consequently, many applications, including urban monitoring, vegetation monitoring, fire and flood damage assessment, etc., could benefit from the high spatial and high spectral resolution images.
To align two images, one technique known as Random Sample Consensus (RANSAC) is shown in a paper by K. G. DERPANIS, “Overview of the RANSAC Algorithm, Lecture Notes, York University, 2010.” RANSAC can be used by two types of features, Speeded Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT), as discussed in the following papers by:    1. H. Bay, A. ESS, T. TUYTELAARS, and L. VAN GOOL, “SURF: Speeded Up Robust Features, Computer Vision and Image Understanding (CVIU), Vol. 110, No. 3, pp. 346-359, 2008,” by; and    2. D. G. LOWE, “Object Recognition From Local Scale-invariant Features, IEEE International Conference on Computer Vision, vol. 2, pp. 1150-1157, 1999.” In this paper, the features are detected in both images, matched, and followed by applying RANSAC to estimate the geometric transformation.
Another more accurate registration algorithm is the Diffeomorphic Image Registration (DIR), as shown in a paper by H. CHEN, A. GOELA, G. J. GARVIN, S. and LI, “A Parameterization of Deformation Fields for Diffeomorphic Image Registration and Its Application to Myocardial Delineation, Medical Image Computing and Computer-Assisted Intervention—MICCAI 2010 Lecture Notes in Computer Science, Volume 6361, 2010, pp 340-348.”
As discussed in another paper by H. KWON and N. M. NARABADI, “Kernel RX-algorithm: A Nonlinear Anomaly Detector for Hyperspectral Imagery, IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 2, February 2005.” The Kernel RX-algorithm is a generalization of the well-known anomaly detection algorithm, known as Reed-Xiaoli (RX) algorithm. When the kernel distance function is defined as the dot product of two vectors, Kernel RX is more flexible than RX, but it is significantly slower.
In the present invention, a novel algorithm can perform a fast approximation of Kernel RX, as disclosed in a paper by J. ZHOU, C. KWAN, B. AYHAN, and M. EISMANN, “A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images, IEEE Trans. Geoscience and Remote Sensing, Volume: 54, Issue: 11, pp. 6497-6504, November 2016.” The novel algorithm is based on clustering, called Cluster Kernel RX (CKRX). As a matter of fact, CKRX is a generalization of Kernel RX (KRX), i.e. CKRX is reduced to Kernel RX under some specific settings.
The basic idea of CKRX is to first cluster the background points and then replace each point with its cluster's center. After replacement, the number of unique points is the number of clusters, which can be very small comparing to the original point set. Although the total number of points does not change, the computation of the anomaly value can be simplified using only the unique cluster centers, which improves the speed by several orders of magnitudes.
The paper mentioned above showed that some Receiver Operating Characteristics (ROC) curves were obtained by using actual hyperspectral images from the Air Force (AF). Many algorithms implemented and compared in that paper. Also, FIG. 11 of the present invention shows the ROC curves, showing that KRX and CKRX gave excellent performance, as their ROC curves almost reach ideal performance.
In surface characterization, accurate material classification is important for mapping out the planet's surface. There are some existing classification algorithms as shown in another paper by C. KWAN, B. AYHAN, G. CHEN, C. CHANG, J. WANG, and B. Ji, “A Novel Approach for Spectral Unmixing, Classification, and Concentration Estimation of Chemical and Biological Agents, IEEE Trans. Geoscience and Remote Sensing, pp. 409-419, vol. 44, no. 2, February 2006.”
In remote sensing domain, a common and successful approach to achieving super resolution is Pan-Sharpening. Pan-Sharpening is an image fusion technique which uses a high resolution single band panchromatic image and low resolution multi-spectral image to produce high resolution multi-spectral images. Compared to multi-view based and example based super-resolution technique, Pan-Sharpening can produce much higher resolution data and is much more reliable and accurate. The Pan-Sharpening idea can also be applied to hyperspectral images, as disclosed in some articles by:    1) J. ZHOU, C. KWAN, and B. BUDAVARI, “Hyperspectral Image Super-Resolution: A Hybrid Color Mapping Approach, SPIE Journal of Applied Remote Sensing, September, 2016”;    2) C. KWAN, J. H. CHOI, S. CHAN, J. ZHOU, and B. BUDAVARI, “Resolution Enhancement for Hyperspectral Images: A Super-Resolution and Fusion Approach, International Conference Acoustics, Speech, and Signal Processing 2017”;    3) C. KWAN, B. BUDAVARI, M. DAO, B. AYHAN, and J. BELL, “Pansharpening of Mastcam images, submitted to 2017 International Geoscience and Remote Sensing Symposium (IGARSS)”; and    4) M. DAO, C. KWAN, B. AYHAN, and J. BELL, “Enhancing Mastcam Images for Mars Rover Mission, submitted to International Symposium on Neural Networks 2017.”